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原文連結
論文資訊
- 類型：已發表論文
- 日期：2024-12-23
摘要
We propose a new performance attribution framework that decomposes a constrained portfolio's holdings, expected returns, variance, expected utility, and realized returns into components attributable to (1) the unconstrained mean-variance optimal portfolio; (2) individual static constraints; and (3) 資訊, if any, arising from those constraints. A key contribution of our framework is the recognition that constraints may contain 資訊 that is correlated with returns, in which case imposing such constraints can affect performance. We extend our framework to accommodate estimation risk in portfolio construction using 貝氏 portfolio analysis, which allows one to select constraints that improve-or are least detrimental to-future performance. We provide simulations and empirical examples involving constr
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